基于IMM-PF的分布式估计融合算法

针对基于扩展卡尔曼滤波的估计融合算法存在线性化误差,且受高斯噪声假设限制的问题,提出一种基于交互式多模型粒子滤波(IMM-PF)的分布式多传感器估计融合算法.各传感器节点采用IMM-PF算法,以便在非线性,非高斯条件下稳健地跟踪机动目标;融合中心则采用基于粒子滤波(PF)的分布式融合方法进行全局估计融合.该算法适用于非线性,非高斯条件下的多传感器状态估计.仿真结果表明,该算法能够提高多传感器系统状态估计的精度.

     

多步历史估计信息反馈多模型融合方法

针对强机动和大观测误差下的目标跟踪问题,传统低阶多模型融合方法存在估计精度较低、鲁棒性较差的缺点;高阶多模型融合方法面临计算量增大和保证实时性之间的矛盾.为此本文针对一类多步稳健机动目标跟踪问题提出一种基于历史估计信息反馈的多模型融合框架,首先累积和反馈历史估计信息,然后结合当前量测计算多阶模型序列的似然函数,最后得到贝叶斯后验融合结果.同时结合粒子滤波构建了易于工程实现的粒子滤波历史反馈多模型融合算法(PF-HFMM).仿真表明,与传统粒子滤波多模型算法相比,本法显著提高了估计精度和鲁棒性.     

基于IMM-PF的分布式估计融合算法

针对基于扩展卡尔曼滤波的估计融合算法存在线性化误差,且受高斯噪声假设限制的问题,提出一种基于交互式多模型粒子滤波(IMM-PF)的分布式多传感器估计融合算法.各传感器节点采用IMM-PF算法,以便在非线性、非高斯条件下稳健地跟踪机动目标;融合中心则采用基于粒子滤波(PF)的分布式融合方法进行全局估计融合.该算法适用于非线性、非高斯条件下的多传感器状态估计.仿真结果表明,该算法能够提高多传感器系统状态估计的精度.     

基于UKF的环境自适应UWB/DR室内定位方法

针对复杂室内环境下超宽带（Ultra WideBand,UWB）信号传播的非视距（Non Line Of Sight,NLOS）误差问题,本文提出了一种基于无迹卡尔曼滤波（Unscented Kalman Filter,UKF）的环境自适应UWB/DR室内定位方法.该方法通过建立自适应UKF滤波模型,将UWB定位信息和航迹推算（Dead Reckoning,DR）定位信息进行融合.依据新息和高斯分布的3σ原则来对UWB定位结果进行非视距检测,再通过新息的实时估计协方差和理论协方差来构建环境适应系数,进而用此系数动态修正UWB定位的观测噪声,使得观测噪声自适应真实环境,降低NLOS误差对融合定位结果的影响.实验结果表明,该方法能有效减小UWB定位的NLOS误差,并且由于环境适应系数的创新引入,比UKF定位和粒子滤波定位（Particle Filtering,PF）有更高的定位精度和更强的抗NLOS误差性能.     

基于FADS/IMU信息融合的大气数据测量方法研究

对飞行器大气数据进行估计是获取飞行状态的重要一环,是实现飞行器控制和稳定飞行的基础。通过研究嵌入式大气数据传感(FADS)系统,提出了基于容积卡尔曼滤波的惯性测量元件(IMU)数据和FADS数据融合算法。该算法对飞行器运动状态建立高阶滤波模型,使用容积点加权求和逼近的方法估计非线性运动模型,滤波输出值经处理后得到马赫数、攻角、侧滑角等大气数据。经仿真实验,算法计算的大气数据较为准确,马赫数误差小于0.01,攻角和侧滑角的误差小于0.1°。     

多输入多输出信道估计算法的仿真研究

研究无线通信,提高数据传输率,是无线通信领域关心的问题.信道估计精度对系统性能有着直接的影响.由于无线通信信道具有时变性,传统信道估计算法很难对时变信道进行准确估计,为了提高多输入多输出信道估计精度,提出一种采用基于粒子滤波的MIMO-OFDM时变信道估计方法.算法首先采用时变信道频率响应的状态空间方程将时变信道估计问题转化为序贯状态估计问题,然后通过粒子滤波算法对序贯状态估计问题进行求解,得到当前时刻的信道状态.仿真结果表明,与传统信道估计方法比较,不仅降低估计均方误差和误码率,估计精度更高,而且很好地提高了信道通信性能.     

基于多传感器数据融合的鲁棒自适应滤波算法

针对具有多传感器量测的动态系统状态有效估计的问题,给出了一种基于多传感器数据融合的鲁棒自适应滤波算法.引入了虚拟量测的概念,实现对于量测数据的预处理,依据模糊集合理论中隶属度的性质定义了一种无需门限设定的数据间置信度函数,以充分提取于测量数据中互补和冗余信息.最后结合卡尔曼滤波中的贝叶斯递推估计原理,完成对于量测方差的在线自适应估计,进而提高了系统状态的滤波精度,并通过计算机仿真验证了算法的有效性.     

基于信息融合的进场段飞行技术误差估计仿真

飞行技术误差的估计精度对导航精度有重要影响,为实现高精度进场着陆操作,需要对飞行技术误差进行精确估计.为此研究了飞行技术误差估计问题,提出基于联邦滤波的信息融合算法,通过对多源传感器信号进行时空配准联邦滤波,实现精度高,完好性强的位置估计,并以此为基础实现飞行技术误差的估计.为了验证提出估计方法的有效性,构建了基于ADS/GPS/ILS/INS等导航源的飞机进场段仿真模型并进行仿真,对飞行技术误差进行了估计,结果表明,提出的方法能够有效实现飞行技术误差的估计.     

多传感器状态融合估计在雷达跟踪中的应用

采用Carlson最优数据融合准则,将基于Kalman滤波的多传感器状态融合佑计方法应用到雷达跟踪系统.仿真实验表明,多传感器Kalman滤波状态融合佑计误差小于单传感器Kalman滤波得出的状态佑计误差,验证了方法对雷达跟踪的有效性.     

基于改进D-S证据理论的室内环境控制决策系统

针对室内环境因子多且相互作用关系复杂,影响室内环境舒适度的控制精准决策,设计了一种基于改进D-S证据理论的室内环境控制决策系统。首先采用箱线图法和均值替代法检测修复异常采集数据,然后利用距离自适应加权融合算法实现同类传感器数据一级融合,最后利用改进D-S证据理论算法,实现全局融合决策。实验结果表明,改进D-S证据理论算法能够有效避免冲突证据带来的融合决策误差,系统可以实现室内环境控制的精准决策,融合决策精度高,具有一定的推广应用价值。     

失配参数在线矫正的永磁同步电机预测电流控制

针对永磁同步电机预测电流控制模型参数失配引起的系统性能下降问题,提出一种基于内模控制观测器的应对策略来矫正模型参数.首先,根据旋转坐标系下的永磁同步电机动态模型,设计了d, q轴电流内模控制观测器并进行稳定性推导证明,观测器可以无静差估计d, q轴电流变化率,进而在线估计电机参数;然后,由卡尔曼滤波减弱参数噪声得到最优参数估计,阈值化处理最优参数估计后在线更新失配参数.最后,在稳态和调速阶段两种不同工况中,将所提策略应用于参数失配的永磁同步电机三矢量预测电流控制;实验结果表明,与同类方法相比,所提策略能在线矫正失配参数,在改善电流波动系数及总谐波畸变率方面表现更好.     

基于粒子滤波的无人机自主轨迹视觉导航控制方法研究

针对现有无人机导航控制方法存在的控制效果不佳的问题,本文提出一种基于粒子滤波的无人机自主轨迹视觉导航控制方法研究。利用粒子滤波算法,实现对无人机自主轨迹视觉导航控制方法的优化设计。采用栅格法构建无人机飞行环境地图,根据无人机的机械组成结构和工作原理,构建运动状态模型。利用内置的摄像机设备采集视觉图像,执行图像灰度转换、几何校正、滤波等预处理步骤。通过对视觉图像的特征提取,判断当前环境是否存在障碍物。利用粒子滤波算法确定无人机位姿,结合障碍物识别结果规划无人机的自主飞行轨迹。将位置、速度和姿态角的控制量计算结果,输入到安装的导航控制器中,完成无人机的自主轨迹视觉导航控制任务。通过实测分析得出结论：应用设计的导航控制方法,其位置误差、速度误差以及姿态角误差均维持在预设值以下,即设计的导航控制方法具有良好的控制效果。     

基于自监督网络的DDPG算法的建筑能耗控制

针对强化学习方法训练能耗控制系统时所存在奖赏稀疏的问题,将一种基于自监督网络的深度确定策略梯度(deep deterministic policy gradient,DDPG)方法应用到建筑能耗控制问题中.首先,处理状态和动作变量作为自监督网络前向模型的输入,预测下一个状态特征向量,同时将预测误差作为好奇心设计内部奖赏,以解决奖赏稀疏问题.然后,采用数据驱动的方法训练建筑能耗模型,构建天气数据作为输入、能耗数据作为输出.最后,利用基于自监督网络的DDPG方法求解最优控制策略,并以此设定空气处理装置(air handling unit,AHU)的最优排放温度,减少设备能耗.实验结果表明,该方法能够在保持建筑环境舒适的基础上,实现较好的节能效果.     

Optimal filtering for linear state delay systems

In this note, the optimal filtering problem for linear systems with state delay over linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the optimal estimate equation similar to the traditional Kalman-Bucy one is derived; however, it is impossible to obtain a system of the filtering equations, that is closed with respect to the only two variables, the optimal estimate and the error variance, as in the Kalman-Bucy filter. The resulting system of equations for determining the error variance consists of a set of equations, whose number is specified by the ratio between the current filtering horizon and the delay value in the state equation and increases as the filtering horizon tends to infinity. In the example, performance of the designed optimal filter for linear systems with state delay is verified against the best Kalman-Bucy filter available for linear systems without delays and two versions of the extended Kalman-Bucy filter for time-delay systems.     

Optimal filtering for linear systems with state and observation delays

In this paper, the optimal filtering problem for linear systems with state and observation delays is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate, error variance, and various error covariances. As a result, the optimal estimate equation similar to the traditional Kalman–Bucy one is derived; however, it is impossible to obtain a system of the filtering equations, that is closed with respect to the only two variables, the optimal estimate and the error variance, as in the Kalman–Bucy filter. The resulting system of equations for determining the filter gain matrix consists, in the general case, of an infinite set of equations. It is however demonstrated that a finite set of the filtering equations, whose number is specified by the ratio between the current filtering horizon and the delay values, can be obtained in the particular case of equal or commensurable (τ=qh, q is natural) delays in the observation and state equations. In the example, performance of the designed optimal filter for linear systems with state and observation delays is verified against the best Kalman–Bucy filter available for linear systems without delays and two versions of the extended Kalman–Bucy filter for time delay systems. Copyright © 2005 John Wiley & Sons, Ltd.     

扩展卡尔曼滤波在受到恶意攻击系统中的状态估计

设计了一种分布式扩展卡尔曼滤波器(Extended Kalman lter,EKF),对非线性目标状态进行估计.在设计过程中,对滤波误差上界进行优化,获得了最优滤波增益.此外,在通信过程中,考虑恶意攻击信号的同时引入了分布式事件触发机制,使得系统在保持一定的估计精度的情况下节省通信资源.最后,以室内的机器人定位问题为例,验证了提出的滤波器的有效性.     

基于LoRa的地下停车场车辆定位系统设计

利用LoRa在抗干扰、低功耗、低成本方面的优势，设计了一种基于LoRa的地下停车场车辆定位系统。针对地下停车场特殊通信环境，对非视距（Not Line of Sight，NLOS）时延模型进行分析。将排序算法与典型均值滤波算法相结合，改善典型均值滤波算法只能将脉冲噪声在滤波窗口内均摊而不能彻底消除的固有缺陷。通过参数拟合的方法，对地下停车场环境下规律性的NLOS时延和节点模块处理误差进行抑制。对三边定位算法进行优化，提高定位精度，避免无解情况的发生。在实际环境中进行测试，验证了系统的可行性。     

Energy Optimization on Wireless-networked Control Systems (W-NCSs) Using Linear Quadratic Gaussian (LQG)

In a wireless networked control system (W-NCS), energy is required to transmit a sensor reading to the controller. It should be noted that the packet success rate (PSR) is an essential factor in the control performance, and PSR is directly proportional to the energy per symbol. Hence, it requires a significant amount of energy to have perfect control performance. However, in most cases in wireless sensor network scenarios, each node is attached to a limited power battery. Therefore, an energy optimization scheme that can harvest energy while maintaining the control performance is essentially required. The combination of Kalman filter and Linear Quadratic Regulator (LQR) that is known as Linear Quadratic Gaussian (LQG) is used as the backbone of the scheme to estimate the state and synthesize the optimal control. In addition, the optimal power scheduler (PS) is introduced to minimize energy usage while maintaining control performance. The finite block length approach is applied to achieve the upper bound of packet error rate. The results of energy consumption optimization showed that the scheme worked perfectly, wherein the energy per symbol usage is low, and the stability of the dynamic system is well maintained.

     

Optimal filtering for polynomial system states with polynomial multiplicative noise

In this paper, the optimal filtering problem for polynomial system states with polynomial multiplicative noise over linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. The procedure for obtaining a closed system of the filtering equations for any polynomial state with polynomial multiplicative noise over linear observations is then established, which yields the explicit closed form of the filtering equations in the particular cases of a linear state equation with linear multiplicative noise and a bilinear state equation with bilinear multiplicative noise. In the example, performance of the designed optimal filter is verified for a quadratic state with a quadratic multiplicative noise over linear observations against the optimal filter for a quadratic state with a state‐independent noise and a conventional extended Kalman–Bucy filter. Copyright © 2006 John Wiley & Sons, Ltd.     

基于改进容积卡尔曼的PMLSM无传感控制研究

在永磁直线同步电机(PMLSM)的无传感器控制系统中,需要对动子的位置和速度进行实时状态估计。针对标准的容积卡尔曼滤波算法在PMLSM无传感控制中存在状态协方差矩阵易失去非负定性,以及噪声统计特性未知时变导致的滤波精度降低甚至发散的问题,提出一种容积卡尔曼滤波的改进算法。该算法结合平方根滤波和改进的渐消型记忆时变噪声估值器特点,能够保证滤波过程中状态协方差阵的非负定性,同时具有应对噪声变化的自适应能力。在永磁同步直线电机的无传感控制仿真实验中,改进的CKF算法能够明显提高标准CKF的滤波精度,在速度跟踪性能上,负载突变前、后的最大跟踪误差百分比分别为0.428 6%、0.146 8%,稳定跟踪后的跟踪误差百分比稳定在0.045 7%。